# dataset settings
dataset_type = 'HRFDataset'
data_root = 'data/HRF'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True)
img_scale = (2336, 3504)
crop_size = (256, 256)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=img_scale,
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]

data = dict(samples_per_gpu=4,
            workers_per_gpu=4,
            train=dict(type='RepeatDataset',
                       times=40000,
                       dataset=dict(type=dataset_type,
                                    data_root=data_root,
                                    img_dir='images/training',
                                    ann_dir='annotations/training',
                                    pipeline=train_pipeline)),
            val=dict(type=dataset_type,
                     data_root=data_root,
                     img_dir='images/validation',
                     ann_dir='annotations/validation',
                     pipeline=test_pipeline),
            test=dict(type=dataset_type,
                      data_root=data_root,
                      img_dir='images/validation',
                      ann_dir='annotations/validation',
                      pipeline=test_pipeline))
